From Data to Decisions: Leveraging EMS Data, AI, and Smart Systems to Improve Care and Operations

Emergency Medical Services generate a continuous stream of high-value clinical and operational data, yet much of it remains underutilized beyond traditional quality assurance. Building on prior work demonstrating EMS data as a public health tool , this session focuses on the next evolution: transforming EMS data into real-time decision support for operations, quality improvement, and equity. Through four practical, field-tested innovations, this session will demonstrate how EMS agencies can unlock the full potential of their data systems: • Emergency Medical Dispatch (EMD) Optimization Dashboard: Using dispatch code–level analytics to better align resource deployment with historical call characteristics and improve system efficiency. • HOPE (Helping Optimize Prehospital Equity): A multi-agency national collaborative integrating standardized demographic and clinical EMS data to identify and address disparities in care. • Suicide AI Detection: Leveraging natural language processing of ePCR narratives to identify suicide-related encounters that are often missed by traditional structured data fields. • Next-Generation CQI Smart Forms: Designing adaptive, data-driven quality improvement tools that surface trends in real time and enhance clinical oversight. Each presenter will walk through the problem, approach, and real-world implementation, with an emphasis on scalability across diverse EMS systems. Attendees will leave with actionable strategies to move beyond retrospective QA and build proactive, data-driven EMS systems that improve care delivery, operational performance, and health equity.